@PhdThesis{EsparciaAlcazar:1998:thesis,
author = "Anna I. EsparciaAlcazar",
title = "Genetic Programming for Adaptive Signal Processing",
school = "Electronics and Electrical Engineering, University of
Glasgow",
year = "1998",
month = jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.elec.gla.ac.uk/~anna/abstract.htm",
size = "pages",
abstract = "This thesis is devoted to presenting the application
of the Genetic Programming (GP) paradigm to a class of
Digital Signal Processing (DSP) problems. Its main
contributions are
a new methodology for representing DiscreteTime
Dynamic Systems (DDS) as expression trees. The
objective is the state space specification of DDSs: the
behaviour of a system for a time instant t_0 is
completely accounted for given the inputs to the system
and also a set of quantities which specify the state of
the system. This means that the proposed method must
incorporate a form of memory that will handle this
information.
For this purpose a number of node types and associated
data structures are defined. These will allow for the
implementation of local and time recursion and also
other specific functions, such as the sigmoid commonly
encountered in neural networks. An example is given by
representing a recurrent NN as an expression tree.
a new approach to the channel equalisation problem. A
survey of existing methods for channel equalisation
reveals that the main shortcoming of these techniques
is that they rely on the assumption of a particular
structure or model for the system addressed. This
implies that knowledge about the system is available;
otherwise the solution obtained will have a poor
performance because it was not well matched to the
problem.
This gives a main motivation for applying GP to channel
equalisation, which is done in this work for the first
time. Firstly, to provide a unified technique for a
wide class of problems, including those which are
poorly understood; and secondly, to find alternative
solutions to those problems which have been
successfully addressed by existing techniques.
In particular, in the equalisation of nonlinear
channels, which have been mainly addressed with Neural
Networks and various adaptation algorithms, the
proposed GP approach presents itself as an interesting
alternative.",
abstract = "a new way of handling numerical parameters in GP, node
gains. A node gain is a numerical parameter associated
to a node that multiplies its output value. This
concept was introduced by Sharman and EsparciaAlcázar
(1993) and is fully developed here.
The motivation for a parameterised GP is addressed,
together with an overview of how it has been addressed
by other authors. The drawbacks of these methods are
highlighted: there is no established way of determining
the number of parameters to use and their placement;
further, unused parameters can be unnecessarily adapted
while, on the other hand, useful ones might be
eliminated. The way in which node gains overcome these
problems is explained. An extra advantage is the
possibility of expressing complex systems in a compact
way, which is labelled {"}compacting effect{"} of node
gains.
The costs of node gains are also pointed out: increase
in the degrees of freedom and increased complexity.
This, in theory, results in an increase of
computational expense, due to the handling of more
complex nodes and to the fact that an extra
multiplication is needed per node. These costs,
however, are expected to be of, at most, the same order
of magnitude as those of the alternatives.
Experimental analysis shows that random node gains may
not be able to achieve all the potential benefits
expected. It is conjectured that optimisation of the
values is needed in order to attain the full benefits
of node gains, which brings along the next
contribution.
a mathematical model is given for an adaptive GP. As
concluded from the previous point, adaptation of the
values of the node gains is needed in order to take
full advantage of them. A Simulated Annealing (SA)
algorithm is introduced as the adaptation algorithm.
This is put in the context of an analogy: the
adaptation of the gains by SA is equivalent to the
learning process of an individual during its
lifetime.
This analogy gives way to the introduction of two
learning schemes, labelled Lamarckian and Darwinian,
which refer to the possibility of inheriting the
learned gains.
The Darwinian and Lamarckian learning schemes for GP
are compared to the standard GP technique and also to
GP with random node gains. Statistical analysis, done
for both fixed and timevarying environments, shows the
superiority of both learning methods over the
nonlearning ones, although it is not possible at this
stage to determine which of the two provides a better
performance.
a number of interesting results in the channel
equalisation problem. These are compared to those
obtained by other techniques and it is concluded that
the proposed method obtains better or similar
performance when extreme values (maximum fitness or
minimum error) are considered.",
}
